ï¿½ cï¿½ï¿½jï¿½ï¿½ï¿½ï¿½^ï¿½dZddlmZddlmZmZm Z mZm ZddlmZmZmZmZddlmZmZmZddlmZmZ m!Z"ddl#m$Z%ddl&m'Z(dd l)m*Z+dd l,m-Z.m/Z0ddl1m1Z2ddl#Z3ddl4Z4 dd l5m6Z7gdï¿½Z:dedï¿½zedï¿½zZ;edï¿½Zde>zZ?dZ@Gdï¿½de4jï¿½ï¿½ZAGdï¿½deAï¿½ZBeAï¿½ZCeCjï¿½ZDeCjï¿½ZEeCjï¿½ZFeCjï¿½ZGeCjï¿½ZHeCjï¿½ZIeCjï¿½ZJeCjï¿½ZKeCjï¿½ZLeCjï¿½ZMeCjï¿½ZNeCjï¿½ZOeCjï¿½ZPeCjï¿½ZQeCjï¿½ZReCjï¿½ZSeCjï¿½ZTeCjï¿½ZUeCjï¿½ZVeCjï¿½ZWeCjï¿½ZXeCjï¿½ZYeCjï¿½ZZeCjï¿½Z[dï¿½Z\d!dï¿½Z]e^e3dï¿½re3jï¿½eCjï¿½ï¿½ï¿½e`d k(re]ï¿½yy#e8$r dd l9m6Z7Yï¿½ï¿½ï¿½wxYw)"aERandom variable generators. bytes ----- uniform bytes (values between 0 and 255) integers -------- uniform within range sequences --------- pick random element pick random sample pick weighted random sample generate random permutation distributions on the real line: ------------------------------ uniform triangular normal (Gaussian) lognormal negative exponential gamma beta pareto Weibull distributions on the circle (angles 0 to 2pi) --------------------------------------------- circular uniform von Mises discrete distributions ---------------------- binomial General notes on the underlying Mersenne Twister core generator: * The period is 2**19937-1. * It is one of the most extensively tested generators in existence. * The random() method is implemented in C, executes in a single Python step, and is, therefore, threadsafe. ï¿½)ï¿½warn)ï¿½logï¿½expï¿½piï¿½eï¿½ceil)ï¿½sqrtï¿½acosï¿½cosï¿½sin)ï¿½tauï¿½floorï¿½isfinite)ï¿½lgammaï¿½fabsï¿½log2)ï¿½urandom)ï¿½Sequence)ï¿½index)ï¿½ accumulateï¿½repeat)ï¿½bisectN)ï¿½sha512)ï¿½Randomï¿½SystemRandomï¿½betavariateï¿½binomialvariateï¿½choiceï¿½choicesï¿½expovariateï¿½gammavariateï¿½gaussï¿½getrandbitsï¿½getstateï¿½lognormvariateï¿½ normalvariateï¿½ paretovariateï¿½ randbytesï¿½randintï¿½randomï¿½ randrangeï¿½sampleï¿½seedï¿½setstateï¿½shuffleï¿½ triangularï¿½uniformï¿½vonmisesvariateï¿½weibullvariateï¿½gï¿½ï¿½@ï¿½@ï¿½ï¿½?ï¿½@ï¿½5ï¿½ï¿½cï¿½ï¿½ï¿½eZdZdZdZd$dï¿½Zd%ï¿½fdï¿½ Zï¿½fdï¿½Zï¿½fdï¿½Zdï¿½Z d ï¿½Z d ï¿½Zdï¿½Zdï¿½Z d ezfdï¿½Ze Zdï¿½Zdefdï¿½Zdï¿½Zdï¿½Zdï¿½Zddï¿½dï¿½Zd$dd dï¿½dï¿½Zdï¿½Zd&dï¿½Zd'dï¿½Zd'dï¿½Zdï¿½Zd(dï¿½Zdï¿½Zdï¿½Z d ï¿½Z!d!ï¿½Z"d"ï¿½Z#d)d#ï¿½Z$ï¿½xZ%S)*raï¿½Random number generator base class used by bound module functions. Used to instantiate instances of Random to get generators that don't share state. Class Random can also be subclassed if you want to use a different basic generator of your own devising: in that case, override the following methods: random(), seed(), getstate(), and setstate(). Optionally, implement a getrandbits() method so that randrange() can cover arbitrarily large ranges. ï¿½Ncï¿½4ï¿½|j|ï¿½d|_y)zeInitialize an instance. Optional argument x controls seeding, as for Random.seed(). N)r-ï¿½ gauss_next)ï¿½selfï¿½xs ï¿½/usr/lib64/python3.12/random.pyï¿½__init__zRandom.__init__~sï¿½ï¿½ ï¿½ ï¿½ ï¿½!ï¿½ï¿½ï¿½ï¿½ï¿½c ï¿½ï¿½ï¿½|dk(rï¿½t|ttfï¿½rpt|tï¿½r|jdï¿½n|}|rt |dï¿½dznd}tt|ï¿½D] }d|z|zdz}ï¿½|t |ï¿½z}|dk(rdn|}nï¿½|d k(rkt|tttfï¿½rPt|tï¿½r|jï¿½}tj|t|ï¿½jï¿½zï¿½}n:t|td ï¿½tttttfï¿½stdï¿½ï¿½t ï¿½|ï¿½E|ï¿½d |_y )a\Initialize internal state from a seed. The only supported seed types are None, int, float, str, bytes, and bytearray. None or no argument seeds from current time or from an operating system specific randomness source if available. If *a* is an int, all bits are used. For version 2 (the default), all of the bits are used if *a* is a str, bytes, or bytearray. For version 1 (provided for reproducing random sequences from older versions of Python), the algorithm for str and bytes generates a narrower range of seeds. r;zlatin-1rï¿½iCBlï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½r:NzOThe only supported seed types are: None, int, float, str, bytes, and bytearray.)ï¿½ isinstanceï¿½strï¿½bytesï¿½decodeï¿½ordï¿½mapï¿½lenï¿½ bytearrayï¿½encodeï¿½intï¿½ from_bytesï¿½_sha512ï¿½digestï¿½typeï¿½floatï¿½ TypeErrorï¿½superr-r?)r@ï¿½aï¿½versionrAï¿½cï¿½ __class__s ï¿½rBr-zRandom.seedï¿½sï¿½ï¿½ï¿½$ï¿½aï¿½<ï¿½Jï¿½qï¿½3ï¿½ï¿½,ï¿½7ï¿½'1ï¿½!ï¿½Uï¿½';ï¿½ï¿½ï¿½ï¿½ï¿½#ï¿½ï¿½Aï¿½"#ï¿½ï¿½Aï¿½aï¿½Dï¿½ ï¿½Qï¿½ï¿½ï¿½Aï¿½ï¿½ï¿½aï¿½[ï¿½ï¿½ï¿½ï¿½kï¿½Qï¿½&ï¿½*<ï¿½<ï¿½ï¿½!ï¿½ ï¿½ï¿½Qï¿½ï¿½Kï¿½Aï¿½ï¿½2ï¿½gï¿½ï¿½1ï¿½Aï¿½ ï¿½ï¿½\ï¿½jï¿½ï¿½Sï¿½%ï¿½ï¿½,Cï¿½Dï¿½ï¿½!ï¿½Sï¿½!ï¿½ï¿½Hï¿½Hï¿½Jï¿½ï¿½ï¿½ï¿½ï¿½qï¿½7ï¿½1ï¿½:ï¿½#4ï¿½#4ï¿½#6ï¿½6ï¿½7ï¿½Aï¿½ï¿½Aï¿½ï¿½Tï¿½ ï¿½Cï¿½ï¿½ï¿½Uï¿½Iï¿½Nï¿½Oï¿½ï¿½Eï¿½Fï¿½ Fï¿½ ï¿½ï¿½ï¿½Qï¿½ï¿½ï¿½ï¿½rDcï¿½Nï¿½ï¿½|jtï¿½|ï¿½ ï¿½|jfS)z9Return internal state; can be passed to setstate() later.)ï¿½VERSIONrYr$r?)r@r]s ï¿½rBr$zRandom.getstateï¿½s ï¿½ï¿½ï¿½ï¿½|ï¿½|ï¿½Uï¿½Wï¿½-ï¿½/ï¿½ï¿½ï¿½ï¿½@ï¿½@rDcï¿½ï¿½ï¿½|d}|dk(r|\}}|_tï¿½|ï¿½ |ï¿½y|dk(r.|\}}|_ tdï¿½|Dï¿½ï¿½}tï¿½|ï¿½ |ï¿½yt d|ï¿½d|jï¿½ï¿½ï¿½ï¿½#t$r}t |ï¿½d}~wwxYw)z:Restore internal state from object returned by getstate().rr=r:c3ï¿½&Kï¿½|] }|dzï¿½ï¿½ï¿½yï¿½w)lNï¿½)ï¿½.0rAs rBï¿½ z"Random.setstate..ï¿½sï¿½ï¿½ï¿½ï¿½%Kï¿½]ï¿½ï¿½aï¿½7ï¿½mï¿½]ï¿½sï¿½Nzstate with version z( passed to Random.setstate() of version )r?rYr.ï¿½tupleï¿½ ValueErrorrXr_)r@ï¿½stater[ï¿½ internalstaterr]s ï¿½rBr.zRandom.setstateï¿½sï¿½ï¿½ï¿½ï¿½ï¿½ï¿½(ï¿½ï¿½ï¿½aï¿½<ï¿½6;ï¿½3ï¿½Gï¿½]ï¿½Dï¿½Oï¿½ï¿½Gï¿½ï¿½]ï¿½+ï¿½ ï¿½ï¿½\ï¿½6;ï¿½3ï¿½Gï¿½]ï¿½Dï¿½Oï¿½ 'ï¿½ %ï¿½%Kï¿½]ï¿½%Kï¿½ Kï¿½ ï¿½ ï¿½Gï¿½ï¿½]ï¿½+ï¿½ï¿½%ï¿½tï¿½|ï¿½|ï¿½5ï¿½6ï¿½ 6ï¿½ï¿½ ï¿½ 'ï¿½ï¿½Qï¿½&ï¿½ï¿½ 'ï¿½sï¿½A5ï¿½5 B ï¿½>Bï¿½B cï¿½"ï¿½|jï¿½Sï¿½N)r$ï¿½r@s rBï¿½__getstate__zRandom.__getstate__ï¿½sï¿½ï¿½ï¿½}ï¿½}ï¿½ï¿½rDcï¿½&ï¿½|j|ï¿½yrj)r.)r@rgs rBï¿½__setstate__zRandom.__setstate__ï¿½sï¿½ï¿½ï¿½ ï¿½ ï¿½eï¿½rDcï¿½<ï¿½|jd|jï¿½fS)Nrb)r]r$rks rBï¿½ __reduce__zRandom.__reduce__ï¿½sï¿½ï¿½ï¿½~ï¿½~ï¿½rï¿½4ï¿½=ï¿½=ï¿½?ï¿½2ï¿½2rDcï¿½ï¿½ï¿½|jD]T}d|jvryd|jvr|j|_yd|jvsï¿½C|j|_yy)aControl how subclasses generate random integers. The algorithm a subclass can use depends on the random() and/or getrandbits() implementation available to it and determines whether it can generate random integers from arbitrarily large ranges. ï¿½ _randbelowr#r*N)ï¿½__mro__ï¿½__dict__ï¿½_randbelow_with_getrandbitsrrï¿½_randbelow_without_getrandbits)ï¿½clsï¿½kwargsr\s rBï¿½__init_subclass__zRandom.__init_subclass__ï¿½sYï¿½ï¿½ï¿½ï¿½ï¿½Aï¿½ï¿½qï¿½zï¿½zï¿½)ï¿½ï¿½ï¿½ï¿½ ï¿½ ï¿½*ï¿½!$ï¿½!@ï¿½!@ï¿½ï¿½ï¿½ï¿½ï¿½1ï¿½:ï¿½:ï¿½%ï¿½!$ï¿½!Cï¿½!Cï¿½ï¿½ï¿½ï¿½rDcï¿½tï¿½|j}|jï¿½}||ï¿½}||k\r||ï¿½}||k\rï¿½|S)z;Return a random int in the range [0,n). Defined for n > 0.)r#ï¿½ bit_length)r@ï¿½nr#ï¿½kï¿½rs rBruz"Random._randbelow_with_getrandbitsï¿½sAï¿½ï¿½ï¿½&ï¿½&ï¿½ï¿½ ï¿½Lï¿½Lï¿½Nï¿½ï¿½ï¿½ï¿½Nï¿½ï¿½ï¿½1ï¿½fï¿½ï¿½Aï¿½ï¿½Aï¿½ï¿½1ï¿½fï¿½ï¿½rDr;cï¿½ï¿½ï¿½|j}||k\rtdï¿½t|ï¿½|zï¿½S||z}||z |z}|ï¿½}||k\r |ï¿½}||k\rï¿½ t||zï¿½|zS)zï¿½Return a random int in the range [0,n). Defined for n > 0. The implementation does not use getrandbits, but only random. zï¿½Underlying random() generator does not supply enough bits to choose from a population range this large. To remove the range limitation, add a getrandbits() method.)r*ï¿½_warnï¿½_floor)r@r|ï¿½maxsizer*ï¿½remï¿½limitr~s rBrvz%Random._randbelow_without_getrandbitsï¿½sï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½<ï¿½ï¿½Nï¿½ Oï¿½ï¿½&ï¿½(ï¿½Qï¿½,ï¿½'ï¿½'ï¿½ï¿½ï¿½kï¿½ï¿½ï¿½3ï¿½ï¿½'ï¿½)ï¿½ï¿½ï¿½Hï¿½ï¿½ï¿½5ï¿½jï¿½ï¿½ï¿½Aï¿½ï¿½5ï¿½jï¿½ï¿½aï¿½'ï¿½kï¿½"ï¿½Qï¿½&ï¿½&rDcï¿½Jï¿½|j|dzï¿½j|dï¿½S)ï¿½Generate n random bytes.ï¿½ï¿½little)r#ï¿½to_bytesï¿½r@r|s rBr(zRandom.randbytess$ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½Aï¿½ï¿½&ï¿½/ï¿½/ï¿½ï¿½8ï¿½<ï¿½t |ï¿½} t|ï¿½D]#}| ||z ï¿½}| |||<| ||z d z | |<ï¿½%|Stï¿½}|j }t|ï¿½D]+}| |ï¿½}||vr | |ï¿½}||vrï¿½ ||ï¿½||||<ï¿½-|Scc} w)afChooses k unique random elements from a population sequence. Returns a new list containing elements from the population while leaving the original population unchanged. The resulting list is in selection order so that all sub-slices will also be valid random samples. This allows raffle winners (the sample) to be partitioned into grand prize and second place winners (the subslices). Members of the population need not be hashable or unique. If the population contains repeats, then each occurrence is a possible selection in the sample. Repeated elements can be specified one at a time or with the optional counts parameter. For example: sample(['red', 'blue'], counts=[4, 2], k=5) is equivalent to: sample(['red', 'red', 'red', 'red', 'blue', 'blue'], k=5) To choose a sample from a range of integers, use range() for the population argument. This is especially fast and space efficient for sampling from a large population: sample(range(10000000), 60) zAPopulation must be a sequence. For dicts or sets, use sorted(d).Nz2The number of counts does not match the populationrzCounts must be integerszCounts must be non-negative)r}z,Sample larger than population or is negativeï¿½ï¿½r4r=r;)rIï¿½ _SequencerXrOï¿½listï¿½_accumulaterfï¿½poprRr,rï¿½ï¿½_bisectrrï¿½_ceilï¿½_logï¿½setï¿½add)r@ï¿½ populationr}rï¿½r|ï¿½ cum_countsï¿½totalï¿½ selectionsrï¿½srï¿½ï¿½resultï¿½setsizeï¿½poolrï¿½rï¿½ï¿½selectedï¿½selected_adds rBr,z Random.samplegsï¿½ï¿½ï¿½jï¿½*ï¿½iï¿½0ï¿½ï¿½@ï¿½Aï¿½ Aï¿½ï¿½ ï¿½Oï¿½ï¿½ï¿½ï¿½ï¿½kï¿½&ï¿½1ï¿½2ï¿½Jï¿½ï¿½:ï¿½ï¿½!ï¿½#ï¿½ ï¿½!Uï¿½Vï¿½Vï¿½(2ï¿½Jï¿½Nï¿½Nï¿½$ï¿½ï¿½Eï¿½ï¿½eï¿½Sï¿½)ï¿½ï¿½ 9ï¿½:ï¿½:ï¿½ï¿½qï¿½yï¿½ ï¿½!>ï¿½?ï¿½?ï¿½ï¿½ï¿½ï¿½Uï¿½5ï¿½\ï¿½Qï¿½ï¿½7ï¿½Jï¿½ï¿½Fï¿½?Iï¿½Jï¿½zï¿½!ï¿½Jï¿½vï¿½jï¿½!ï¿½4ï¿½5ï¿½zï¿½Jï¿½Jï¿½ï¿½Oï¿½Oï¿½ ï¿½ï¿½Aï¿½{ï¿½ï¿½{ï¿½ï¿½Kï¿½Lï¿½Lï¿½ï¿½ï¿½Kï¿½Lï¿½Lï¿½ï¿½ï¿½!ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½qï¿½5ï¿½ï¿½qï¿½Eï¿½$ï¿½qï¿½1ï¿½uï¿½aï¿½.ï¿½1ï¿½1ï¿½1ï¿½Gï¿½ï¿½ï¿½<ï¿½ï¿½ ï¿½#ï¿½Dï¿½ï¿½1ï¿½Xï¿½ï¿½ï¿½aï¿½!ï¿½eï¿½$ï¿½ï¿½ ï¿½ï¿½Gï¿½ï¿½qï¿½ ï¿½ï¿½qï¿½1ï¿½uï¿½qï¿½yï¿½/ï¿½ï¿½Qï¿½ï¿½ï¿½ï¿½ ï¿½ï¿½uï¿½Hï¿½#ï¿½<ï¿½<ï¿½Lï¿½ï¿½1ï¿½Xï¿½ï¿½ï¿½aï¿½Lï¿½ï¿½ï¿½8ï¿½mï¿½!ï¿½!ï¿½ï¿½Aï¿½ï¿½8ï¿½mï¿½ï¿½Qï¿½ï¿½&ï¿½qï¿½Mï¿½ï¿½qï¿½ ï¿½ï¿½ï¿½ ï¿½ï¿½3Ksï¿½;F>)ï¿½cum_weightsr}c ï¿½\ï¿½|j}t|ï¿½}|ï¿½N|ï¿½6t}|dz }td|ï¿½Dï¿½cgc]}|||ï¿½|zï¿½ï¿½ï¿½c}S t t|ï¿½ï¿½}n |ï¿½t dï¿½ï¿½t|ï¿½|k7rtdï¿½ï¿½|ddz} | dkrtdï¿½ï¿½t| ï¿½stdï¿½ï¿½t} |d z }td|ï¿½Dï¿½cgc]}|| ||ï¿½| zd |ï¿½ï¿½ï¿½c}Scc}w#t$r#t|tï¿½sï¿½|}t d|ï¿½ï¿½ï¿½dï¿½wxYwcc}w)zï¿½Return a k sized list of population elements chosen with replacement. If the relative weights or cumulative weights are not specified, the selections are made with equal probability. Nï¿½z4The number of choices must be a keyword argument: k=z2Cannot specify both weights and cumulative weightsz3The number of weights does not match the populationrGz*Total of weights must be greater than zerozTotal of weights must be finiter;r)r*rOrï¿½ï¿½_repeatrï¿½rï¿½rXrIrRrfï¿½ _isfiniterï¿½)r@rï¿½ï¿½weightsrï¿½r}r*r|rrï¿½rï¿½rï¿½his rBrzRandom.choicesï¿½sfï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ ï¿½Oï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½Sï¿½ï¿½ï¿½AHï¿½ï¿½qï¿½AQï¿½Rï¿½AQï¿½Aï¿½ ï¿½5ï¿½ï¿½ï¿½Aï¿½ï¿½#6ï¿½7ï¿½AQï¿½Rï¿½Rï¿½ ï¿½"ï¿½;ï¿½wï¿½#7ï¿½8ï¿½ï¿½ï¿½ ï¿½ï¿½Pï¿½Qï¿½Qï¿½ï¿½{ï¿½ï¿½qï¿½ ï¿½ï¿½Rï¿½Sï¿½Sï¿½ï¿½Bï¿½ï¿½#ï¿½%ï¿½ï¿½ï¿½Cï¿½<ï¿½ï¿½Iï¿½Jï¿½Jï¿½ï¿½ï¿½ï¿½ï¿½>ï¿½?ï¿½?ï¿½ï¿½ï¿½ ï¿½ï¿½Uï¿½ï¿½ ï¿½ï¿½qï¿½)ï¿½+ï¿½)ï¿½Aï¿½ï¿½6ï¿½+ï¿½vï¿½xï¿½%ï¿½/?ï¿½ï¿½Bï¿½Gï¿½Hï¿½)ï¿½+ï¿½ +ï¿½ï¿½+Sï¿½ï¿½ï¿½ ï¿½!ï¿½'ï¿½3ï¿½/ï¿½ï¿½ï¿½ï¿½ï¿½Kï¿½ï¿½ï¿½Mï¿½ï¿½ï¿½ï¿½ ï¿½ï¿½$+sï¿½C5ï¿½C:ï¿½D)ï¿½:,D&cï¿½4ï¿½|||z |jï¿½zzS)zï¿½Get a random number in the range [a, b) or [a, b] depending on rounding. The mean (expected value) and variance of the random variable are: E[X] = (a + b) / 2 Var[X] = (b - a) ** 2 / 12 ï¿½r*rï¿½s rBr1zRandom.uniformï¿½sï¿½ï¿½ï¿½Aï¿½ï¿½Eï¿½Tï¿½[ï¿½[ï¿½]ï¿½*ï¿½*ï¿½*rDcï¿½ï¿½|jï¿½} |ï¿½dn ||z ||z z}||kDrd|z }d|z }||}}|||z t||zï¿½zzS#t$r|cYSwxYw)aï¿½Triangular distribution. Continuous distribution bounded by given lower and upper limits, and having a given mode value in-between. http://en.wikipedia.org/wiki/Triangular_distribution The mean (expected value) and variance of the random variable are: E[X] = (low + high + mode) / 3 Var[X] = (low**2 + high**2 + mode**2 - low*high - low*mode - high*mode) / 18 ï¿½ï¿½?r7)r*ï¿½ZeroDivisionErrorï¿½_sqrt)r@ï¿½lowï¿½highï¿½modeï¿½ur\s rBr0zRandom.triangularï¿½sï¿½ï¿½ï¿½ ï¿½Kï¿½Kï¿½Mï¿½ï¿½ ï¿½ï¿½|ï¿½ï¿½$ï¿½ï¿½*ï¿½ï¿½ï¿½ï¿½)Dï¿½Aï¿½ ï¿½qï¿½5ï¿½ï¿½aï¿½ï¿½Aï¿½ï¿½aï¿½ï¿½Aï¿½ï¿½cï¿½ï¿½Cï¿½ï¿½dï¿½Sï¿½jï¿½Eï¿½!ï¿½aï¿½%ï¿½Lï¿½0ï¿½0ï¿½0ï¿½ï¿½ !ï¿½ ï¿½ï¿½Jï¿½ ï¿½sï¿½Aï¿½Aï¿½Acï¿½ï¿½|j} |ï¿½}d|ï¿½z }t|dz z|z}||zdz}|t|ï¿½krnï¿½9|||zzS)z\Normal distribution. mu is the mean, and sigma is the standard deviation. r7rï¿½r6)r*ï¿½ NV_MAGICCONSTrï¿½)r@ï¿½muï¿½sigmar*ï¿½u1ï¿½u2ï¿½zï¿½zzs rBr&zRandom.normalvariatesgï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½Bï¿½ï¿½vï¿½xï¿½ï¿½Bï¿½ï¿½ï¿½cï¿½ï¿½*ï¿½Rï¿½/ï¿½Aï¿½ï¿½Qï¿½ï¿½ï¿½ï¿½Bï¿½ï¿½dï¿½2ï¿½hï¿½Yï¿½ï¿½ï¿½ ï¿½ï¿½Aï¿½ï¿½Iï¿½~ï¿½rDcï¿½ï¿½ï¿½|j}|j}d|_|ï¿½N|ï¿½tz}tdt d|ï¿½z ï¿½zï¿½}t|ï¿½|z}t |ï¿½|z|_|||zzS)zï¿½Gaussian distribution. mu is the mean, and sigma is the standard deviation. This is slightly faster than the normalvariate() function. Not thread-safe without a lock around calls. Ngï¿½r7)r*r?ï¿½TWOPIrï¿½rï¿½ï¿½_cosï¿½_sin)r@rï¿½rï¿½r*rï¿½ï¿½x2piï¿½g2rads rBr"zRandom.gauss'stï¿½ï¿½6ï¿½ï¿½ï¿½ï¿½ï¿½Oï¿½Oï¿½ï¿½ï¿½ï¿½ï¿½ï¿½9ï¿½ï¿½8ï¿½eï¿½#ï¿½Dï¿½ï¿½$ï¿½ï¿½cï¿½Fï¿½Hï¿½nï¿½!5ï¿½5ï¿½6ï¿½Eï¿½ï¿½Tï¿½ ï¿½Uï¿½"ï¿½Aï¿½"ï¿½4ï¿½jï¿½5ï¿½0ï¿½Dï¿½Oï¿½ï¿½Aï¿½ï¿½Iï¿½~ï¿½rDcï¿½8ï¿½t|j||ï¿½ï¿½S)zï¿½Log normal distribution. If you take the natural logarithm of this distribution, you'll get a normal distribution with mean mu and standard deviation sigma. mu can have any value, and sigma must be greater than zero. )ï¿½_expr&)r@rï¿½rï¿½s rBr%zRandom.lognormvariateMsï¿½ï¿½ï¿½Dï¿½&ï¿½&ï¿½rï¿½5ï¿½1ï¿½2ï¿½2rDcï¿½Bï¿½td|jï¿½z ï¿½|zS)aï¿½Exponential distribution. lambd is 1.0 divided by the desired mean. It should be nonzero. (The parameter would be called "lambda", but that is a reserved word in Python.) Returned values range from 0 to positive infinity if lambd is positive, and from negative infinity to 0 if lambd is negative. The mean (expected value) and variance of the random variable are: E[X] = 1 / lambd Var[X] = 1 / lambd ** 2 r7)rï¿½r*)r@ï¿½lambds rBr zRandom.expovariateWs"ï¿½ï¿½$ï¿½Sï¿½4ï¿½;ï¿½;ï¿½=ï¿½(ï¿½)ï¿½)ï¿½Eï¿½1ï¿½1rDcï¿½ï¿½|j}|dkrt|ï¿½zSd|z}|td||zzï¿½z} |ï¿½}tt|zï¿½}|||zz}|ï¿½} | d||zz ks| d|z t|ï¿½zkrnï¿½Id|z} | |zd| |zzz}|ï¿½}|dkDr|t |ï¿½ztz} | S|t |ï¿½z tz} | S)aFCircular data distribution. mu is the mean angle, expressed in radians between 0 and 2*pi, and kappa is the concentration parameter, which must be greater than or equal to zero. If kappa is equal to zero, this distribution reduces to a uniform random angle over the range 0 to 2*pi. gï¿½ï¿½ï¿½ï¿½ï¿½Æ°>rï¿½r7)r*rï¿½rï¿½rï¿½ï¿½_pirï¿½ï¿½_acos)r@rï¿½ï¿½kappar*rï¿½r~rï¿½rï¿½ï¿½drï¿½ï¿½qï¿½fï¿½u3ï¿½thetas rBr2zRandom.vonmisesvariateksï¿½ï¿½ï¿½ ï¿½ï¿½ï¿½ï¿½ï¿½Dï¿½=ï¿½ï¿½6ï¿½8ï¿½#ï¿½#ï¿½ï¿½%ï¿½Kï¿½ï¿½ ï¿½ï¿½cï¿½Aï¿½ï¿½Eï¿½kï¿½"ï¿½"ï¿½ï¿½ï¿½ï¿½ï¿½Bï¿½ï¿½Sï¿½2ï¿½Xï¿½ï¿½Aï¿½ï¿½Qï¿½ï¿½Uï¿½ï¿½Aï¿½ï¿½ï¿½Bï¿½ï¿½Cï¿½!ï¿½aï¿½%ï¿½Kï¿½ï¿½2ï¿½#ï¿½ï¿½'ï¿½Tï¿½!ï¿½Wï¿½)<ï¿½#<ï¿½ï¿½ï¿½ ï¿½!ï¿½Gï¿½ï¿½ ï¿½ï¿½Uï¿½sï¿½Qï¿½ï¿½Uï¿½{ï¿½#ï¿½ï¿½ ï¿½Xï¿½ï¿½ ï¿½ï¿½8ï¿½ï¿½%ï¿½ï¿½(ï¿½]ï¿½eï¿½+ï¿½Eï¿½ï¿½ï¿½ï¿½%ï¿½ï¿½(ï¿½]ï¿½eï¿½+ï¿½Eï¿½ï¿½rDcï¿½ï¿½|dks|dkrtdï¿½ï¿½|j}|dkDrï¿½td|zdz ï¿½}|tz }||z} |ï¿½}d|cxkrdksnï¿½d|ï¿½z }t |d|z zï¿½|z} |t| ï¿½z} ||z|z}||| zz| z }|tzd|zz dk\s|t |ï¿½k\r| |zSï¿½z|dk(rt d|ï¿½z ï¿½|zS |ï¿½} t|ztz}|| z}|dkr |d|zz} nt ||z |zï¿½} |ï¿½}|dkDr|| |dz zkr | |zS|t| ï¿½kr | |zSï¿½o)aï¿½Gamma distribution. Not the gamma function! Conditions on the parameters are alpha > 0 and beta > 0. The probability distribution function is: x ** (alpha - 1) * math.exp(-x / beta) pdf(x) = -------------------------------------- math.gamma(alpha) * beta ** alpha The mean (expected value) and variance of the random variable are: E[X] = alpha * beta Var[X] = alpha * beta ** 2 rï¿½z*gammavariate: alpha and beta must be > 0.0r7r5gHï¿½ï¿½ï¿½ï¿½ï¿½z>gï¿½Pï¿½ï¿½ï¿½ï¿½?r8)rfr*rï¿½ï¿½LOG4rï¿½rï¿½ï¿½ SG_MAGICCONSTï¿½_e)r@ï¿½alphaï¿½betar*ï¿½ainvï¿½bbbï¿½cccrï¿½rï¿½ï¿½vrArï¿½r~rï¿½rï¿½ï¿½ps rBr!zRandom.gammavariateï¿½sï¿½ï¿½ï¿½(ï¿½Cï¿½<ï¿½4ï¿½3ï¿½;ï¿½ï¿½Iï¿½Jï¿½Jï¿½ï¿½ï¿½ï¿½ï¿½ï¿½3ï¿½;ï¿½ï¿½ï¿½uï¿½ï¿½sï¿½*ï¿½+ï¿½Dï¿½ï¿½$ï¿½,ï¿½Cï¿½ï¿½$ï¿½,ï¿½Cï¿½ï¿½ï¿½Xï¿½ï¿½ï¿½bï¿½,ï¿½9ï¿½,ï¿½ï¿½ï¿½6ï¿½8ï¿½^ï¿½ï¿½ï¿½ï¿½sï¿½Rï¿½xï¿½ï¿½)ï¿½Dï¿½0ï¿½ï¿½ï¿½Dï¿½ï¿½Gï¿½Oï¿½ï¿½ï¿½ï¿½Gï¿½bï¿½Lï¿½ï¿½ï¿½#ï¿½ï¿½'ï¿½Mï¿½Aï¿½%ï¿½ï¿½ï¿½}ï¿½$ï¿½sï¿½Qï¿½wï¿½.ï¿½#ï¿½5ï¿½ï¿½dï¿½1ï¿½gï¿½ï¿½ï¿½tï¿½8ï¿½Oï¿½ï¿½ï¿½cï¿½\ï¿½ï¿½ï¿½vï¿½xï¿½ï¿½(ï¿½(ï¿½4ï¿½/ï¿½/ï¿½ ï¿½ï¿½Hï¿½ï¿½ï¿½%ï¿½Zï¿½2ï¿½%ï¿½ï¿½ï¿½ï¿½Eï¿½ï¿½ï¿½ï¿½8ï¿½ï¿½cï¿½Eï¿½kï¿½*ï¿½Aï¿½ï¿½qï¿½1ï¿½uï¿½ï¿½oï¿½.ï¿½.ï¿½Aï¿½ï¿½Xï¿½ï¿½ï¿½sï¿½7ï¿½ï¿½Qï¿½5ï¿½3ï¿½;ï¿½/ï¿½/ï¿½ï¿½ï¿½tï¿½8ï¿½Oï¿½ï¿½4ï¿½ï¿½ï¿½8ï¿½^ï¿½ï¿½ï¿½tï¿½8ï¿½Oï¿½rDcï¿½\ï¿½|j|dï¿½}|r|||j|dï¿½zzSy)aQBeta distribution. Conditions on the parameters are alpha > 0 and beta > 0. Returned values range between 0 and 1. The mean (expected value) and variance of the random variable are: E[X] = alpha / (alpha + beta) Var[X] = alpha * beta / ((alpha + beta)**2 * (alpha + beta + 1)) r7rï¿½)r!)r@rï¿½rï¿½ï¿½ys rBrzRandom.betavariateï¿½s9ï¿½ï¿½6 ï¿½ï¿½ï¿½eï¿½Sï¿½)ï¿½ï¿½ï¿½ï¿½ï¿½Dï¿½-ï¿½-ï¿½dï¿½Cï¿½8ï¿½8ï¿½9ï¿½9ï¿½rDcï¿½8ï¿½d|jï¿½z }|d|zzS)z3Pareto distribution. alpha is the shape parameter.r7gï¿½rï¿½)r@rï¿½rï¿½s rBr'zRandom.paretovariateï¿½s#ï¿½ï¿½ ï¿½$ï¿½+ï¿½+ï¿½-ï¿½ï¿½ï¿½ï¿½Tï¿½Eï¿½\ï¿½"ï¿½"rDcï¿½Rï¿½d|jï¿½z }|t|ï¿½d|zzzS)zfWeibull distribution. alpha is the scale parameter and beta is the shape parameter. r7)r*rï¿½)r@rï¿½rï¿½rï¿½s rBr3zRandom.weibullvariates.ï¿½ï¿½ ï¿½$ï¿½+ï¿½+ï¿½-ï¿½ï¿½ï¿½ï¿½ï¿½aï¿½ï¿½ï¿½cï¿½Dï¿½jï¿½1ï¿½1ï¿½1rDcï¿½ï¿½ï¿½|dkrtdï¿½ï¿½|dks|dk\r|dk(ry|dk(r|Stdï¿½ï¿½|j}|dk(rt|ï¿½|kï¿½S|dkDr||j|d|z ï¿½z S||zdkrFdx}}t d|z ï¿½}|s|S |tt |ï¿½ï¿½|zï¿½dzz }||kDr|S|dz }ï¿½/||zdk\r|dksJï¿½d }t ||zd|z zï¿½}dd|zz} d d| zzd|zz} ||zdz}dd| zz } |ï¿½}|dz}dt|ï¿½z } td| z| z| z|z|zï¿½}|dks||kDrï¿½@|ï¿½}| dk\r||kr|S|sOdd| zz|z}t|d|z zï¿½}t|dz|zï¿½}t|dzï¿½t||z dzï¿½z}d }|| | | zz| zzz}t|ï¿½t|dzï¿½z t||z dzï¿½z |z zzkr|Sï¿½ï¿½)aï¿½Binomial random variable. Gives the number of successes for *n* independent trials with the probability of success in each trial being *p*: sum(random() < p for i in range(n)) Returns an integer in the range: 0 <= X <= n The mean (expected value) and variance of the random variable are: E[X] = n * p Var[x] = n * p * (1 - p) rzn must be non-negativerï¿½r7z&p must be in the range 0.0 <= p <= 1.0r;rï¿½g$@TFgffffffï¿½?g= ×£p=@gEï¿½ï¿½ï¿½JYï¿½ï¿½gaï¿½ï¿½+eï¿½?ï¿½{ï¿½Gï¿½zï¿½?gq= ×£pï¿½?gï¿½ï¿½ï¿½ï¿½ï¿½ï¿½@r5gï¿½Qï¿½ï¿½ï¿½?gï¿½p= ×£@gffffff@) rfr*rï¿½rï¿½_log2rï¿½rï¿½ï¿½_fabsrï¿½ï¿½_lgamma)r@r|rï¿½r*rArï¿½r\ï¿½setup_completeï¿½spqrï¿½rZï¿½vrrï¿½ï¿½usr}rï¿½rï¿½ï¿½lpqï¿½mï¿½hs rBrzRandom.binomialvariatesï¿½ï¿½ï¿½" ï¿½qï¿½5ï¿½ï¿½5ï¿½6ï¿½6ï¿½ï¿½ï¿½8ï¿½qï¿½Cï¿½xï¿½ï¿½Cï¿½xï¿½ï¿½ï¿½Cï¿½xï¿½ï¿½ï¿½ï¿½Eï¿½Fï¿½Fï¿½ï¿½ï¿½ï¿½ï¿½ ï¿½ï¿½6ï¿½ï¿½&ï¿½(ï¿½Qï¿½,ï¿½'ï¿½'ï¿½ ï¿½sï¿½7ï¿½ï¿½tï¿½+ï¿½+ï¿½Aï¿½sï¿½Qï¿½wï¿½7ï¿½7ï¿½7ï¿½ï¿½qï¿½5ï¿½4ï¿½<ï¿½ï¿½Iï¿½Aï¿½ï¿½ï¿½cï¿½Aï¿½gï¿½ï¿½Aï¿½ï¿½ï¿½ï¿½ï¿½ï¿½Vï¿½Eï¿½&ï¿½(ï¿½Oï¿½aï¿½/ï¿½0ï¿½1ï¿½4ï¿½4ï¿½ï¿½ï¿½qï¿½5ï¿½ï¿½Hï¿½ï¿½Qï¿½ï¿½ï¿½ ï¿½ï¿½ï¿½sï¿½dï¿½{ï¿½qï¿½Cï¿½xï¿½'ï¿½'ï¿½ï¿½ï¿½ï¿½Aï¿½ï¿½Eï¿½Sï¿½1ï¿½Wï¿½%ï¿½&ï¿½ï¿½ï¿½4ï¿½#ï¿½:ï¿½ï¿½ï¿½ï¿½fï¿½qï¿½jï¿½ ï¿½4ï¿½!ï¿½8ï¿½+ï¿½ï¿½ ï¿½ï¿½Eï¿½Cï¿½Kï¿½ï¿½ ï¿½Cï¿½!ï¿½Gï¿½^ï¿½ï¿½ï¿½ï¿½ï¿½Aï¿½ ï¿½ï¿½Hï¿½Aï¿½ï¿½uï¿½Qï¿½xï¿½ï¿½Bï¿½ï¿½ï¿½aï¿½ï¿½"ï¿½ï¿½qï¿½(ï¿½Aï¿½-ï¿½ï¿½1ï¿½2ï¿½Aï¿½ï¿½1ï¿½uï¿½ï¿½Aï¿½ï¿½ï¿½ï¿½ï¿½Aï¿½ï¿½Tï¿½zï¿½aï¿½2ï¿½gï¿½ï¿½ï¿½ "ï¿½ï¿½ï¿½aï¿½ï¿½ï¿½3ï¿½.ï¿½ï¿½ï¿½1ï¿½ï¿½aï¿½ï¿½=ï¿½)ï¿½ï¿½ï¿½Aï¿½ï¿½Eï¿½Qï¿½;ï¿½'ï¿½ï¿½ï¿½Aï¿½ï¿½Eï¿½Nï¿½Wï¿½Qï¿½ï¿½Uï¿½Qï¿½Yï¿½%7ï¿½7ï¿½ï¿½!%ï¿½ï¿½ ï¿½ï¿½!ï¿½rï¿½Bï¿½wï¿½-ï¿½!ï¿½+ï¿½,ï¿½,ï¿½Aï¿½ï¿½Aï¿½wï¿½!ï¿½gï¿½aï¿½!ï¿½eï¿½nï¿½,ï¿½wï¿½qï¿½1ï¿½uï¿½qï¿½yï¿½/Aï¿½Aï¿½Qï¿½ï¿½Uï¿½cï¿½Mï¿½Qï¿½Qï¿½ï¿½ï¿½5rDrj)Nr:)rï¿½r7Nï¿½rï¿½r7)r7)r;rï¿½)&ï¿½__name__ï¿½ __module__ï¿½__qualname__ï¿½__doc__r_rCr-r$r.rlrnrpryruï¿½BPFrvrrr(rï¿½r+r)rr/r,rr1r0r&r"r%r r2r!rr'r3rï¿½ __classcell__)r]s@rBrrnsï¿½ï¿½ï¿½ï¿½ï¿½ï¿½Gï¿½ï¿½$ï¿½LAï¿½6ï¿½Bï¿½ï¿½3ï¿½ï¿½(ï¿½9:ï¿½3ï¿½ï¿½'ï¿½&-ï¿½Jï¿½=ï¿½%)ï¿½tï¿½'3ï¿½R&ï¿½.ï¿½$ï¿½/3ï¿½]ï¿½~#+ï¿½tï¿½qï¿½#+ï¿½P +ï¿½1ï¿½2ï¿½*$ï¿½L3ï¿½2ï¿½((ï¿½TCï¿½Jï¿½@#ï¿½ 2ï¿½VrDrcï¿½6ï¿½eZdZdZdï¿½Zdï¿½Zdï¿½Zdï¿½Zdï¿½ZexZ Z y)rzï¿½Alternate random number generator using sources provided by the operating system (such as /dev/urandom on Unix or CryptGenRandom on Windows). Not available on all systems (see os.urandom() for details). cï¿½Rï¿½tjtdï¿½ï¿½dz tzS)z7Get the next random number in the range 0.0 <= X < 1.0.rFr=)rRrSï¿½_urandomï¿½ RECIP_BPFrks rBr*zSystemRandom.randomusï¿½ï¿½ï¿½ï¿½ï¿½xï¿½ï¿½{ï¿½+ï¿½qï¿½0ï¿½Iï¿½=ï¿½=rDcï¿½ï¿½|dkrtdï¿½ï¿½|dzdz}tjt|ï¿½ï¿½}||dz|z z S)z:getrandbits(k) -> x. Generates an int with k random bits.rz#number of bits must be non-negativerFrï¿½)rfrRrSr )r@r}ï¿½numbytesrAs rBr#zSystemRandom.getrandbitsysIï¿½ï¿½ï¿½qï¿½5ï¿½ï¿½Bï¿½Cï¿½Cï¿½ï¿½ï¿½Eï¿½aï¿½<ï¿½ï¿½ï¿½Nï¿½Nï¿½8ï¿½Hï¿½-ï¿½.ï¿½ï¿½ï¿½Xï¿½ï¿½\ï¿½Aï¿½%ï¿½&ï¿½&rDcï¿½ï¿½t|ï¿½S)rï¿½)r rï¿½s rBr(zSystemRandom.randbytesï¿½sï¿½ï¿½ï¿½ï¿½{ï¿½rDcï¿½ï¿½y)zï¿½'ï¿½ï¿½ï¿½Pï¿½*ï¿½)ï¿½Hï¿½xrDrcï¿½6ï¿½ddlm}m}ddlm}|ï¿½}td|ï¿½Dï¿½cgc]}||ï¿½ï¿½ï¿½ }}|ï¿½} ||ï¿½} ||| ï¿½}t |ï¿½}t|ï¿½} t| |z dï¿½d|ï¿½d|jï¿½|ï¿½ï¿½ï¿½td| ||| fzï¿½ycc}w)Nr)ï¿½stdevï¿½fmean)ï¿½perf_counterz.3fz sec, z times z"avg %g, stddev %g, min %g, max %g ) ï¿½ statisticsrrï¿½timerrï¿½ï¿½minï¿½maxï¿½printr)r|ï¿½funcrrï¿½meanrï¿½t0rï¿½ï¿½dataï¿½t1ï¿½xbarrï¿½rï¿½rï¿½s rBï¿½_test_generatorr$ï¿½sï¿½ï¿½ï¿½/ï¿½!ï¿½ ï¿½ï¿½Bï¿½!(ï¿½ï¿½qï¿½!1ï¿½2ï¿½!1ï¿½Aï¿½Dï¿½$ï¿½Kï¿½!1ï¿½Dï¿½2ï¿½ ï¿½ï¿½Bï¿½ï¿½ï¿½:ï¿½Dï¿½ï¿½$ï¿½ï¿½ï¿½Eï¿½ ï¿½dï¿½)ï¿½Cï¿½ï¿½tï¿½9ï¿½Dï¿½ ï¿½Rï¿½"ï¿½Wï¿½Sï¿½Mï¿½ï¿½ï¿½sï¿½'ï¿½$ï¿½-ï¿½-ï¿½ï¿½ï¿½ï¿½ Aï¿½Bï¿½ ï¿½ /ï¿½4ï¿½ï¿½ï¿½Tï¿½2Jï¿½ Jï¿½Kï¿½ï¿½3sï¿½Bcï¿½hï¿½t|tdï¿½t|tdï¿½t|tdï¿½t|tdï¿½t|t dï¿½t|t dï¿½t|tdï¿½t|tdï¿½t|tdï¿½t|tdï¿½t|td ï¿½t|td ï¿½t|tdï¿½t|tdï¿½t|td ï¿½t|tdï¿½t|tdï¿½t|tdï¿½y)Nrbr)ï¿½g333333ï¿½?)ï¿½dgï¿½?)rï¿½r7)çš™ï¿½ï¿½ï¿½ï¿½ï¿½?r7)r(r5)rï¿½r7)gï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½?r7)r7r7)r5r7)g4@r7)gi@r7)ï¿½@r))rï¿½r7gUUUUUUï¿½?) r$r*r&r%r2rr!r"rr0)ï¿½Ns rBï¿½_testr+ï¿½sï¿½ï¿½ï¿½ï¿½Aï¿½vï¿½rï¿½"ï¿½ï¿½Aï¿½}ï¿½jï¿½1ï¿½ï¿½Aï¿½~ï¿½zï¿½2ï¿½ï¿½Aï¿½ï¿½ ï¿½3ï¿½ï¿½Aï¿½ï¿½ ï¿½3ï¿½ï¿½Aï¿½ï¿½ï¿½4ï¿½ï¿½Aï¿½|ï¿½[ï¿½1ï¿½ï¿½Aï¿½|ï¿½Zï¿½0ï¿½ï¿½Aï¿½|ï¿½Zï¿½0ï¿½ï¿½Aï¿½|ï¿½Zï¿½0ï¿½ï¿½Aï¿½|ï¿½Zï¿½0ï¿½ï¿½Aï¿½|ï¿½Zï¿½0ï¿½ï¿½Aï¿½|ï¿½Zï¿½0ï¿½ï¿½Aï¿½|ï¿½[ï¿½1ï¿½ï¿½Aï¿½|ï¿½\ï¿½2ï¿½ï¿½Aï¿½uï¿½jï¿½)ï¿½ï¿½Aï¿½{ï¿½Jï¿½/ï¿½ï¿½Aï¿½zï¿½#8ï¿½9rDï¿½fork)ï¿½after_in_childï¿½__main__)i')arï¿½warningsrrï¿½ï¿½mathrrï¿½rrï¿½rrï¿½rrï¿½rrï¿½r rï¿½r rï¿½rrï¿½rrï¿½r rï¿½rrï¿½rrï¿½rrï¿½rrï¿½rrï¿½ï¿½osrr ï¿½_collections_abcrrï¿½ï¿½operatorrrï¿½ï¿½ itertoolsrrï¿½rrï¿½rrï¿½ï¿½_osï¿½_randomï¿½_sha2rrTï¿½ImportErrorï¿½hashlibï¿½__all__rï¿½rï¿½rï¿½rr rï¿½rrï¿½_instr-r*r1r0r)rr+r,r/rr&r%r r2r!r"rrr'r3r$r.r#r(r$r+ï¿½hasattrï¿½register_at_forkrrbrDrBï¿½r>sï¿½ï¿½ï¿½.ï¿½h#ï¿½Lï¿½Lï¿½Gï¿½Gï¿½Eï¿½Eï¿½@ï¿½@ï¿½"ï¿½2ï¿½$ï¿½Bï¿½$ï¿½ï¿½ï¿½*ï¿½'ï¿½ ï¿½ï¿½:ï¿½Dï¿½ï¿½Jï¿½ï¿½ï¿½sï¿½ï¿½+ï¿½ ï¿½ï¿½Cï¿½yï¿½ï¿½ï¿½dï¿½3ï¿½iï¿½ï¿½ ï¿½ï¿½ï¿½ ï¿½#ï¿½ï¿½Iï¿½ ï¿½ï¿½ï¿½wï¿½Wï¿½^ï¿½^ï¿½wï¿½|"*ï¿½6ï¿½"*ï¿½X ï¿½ï¿½ï¿½ï¿½zï¿½zï¿½ï¿½ ï¿½ï¿½ï¿½ï¿½ ï¿½-ï¿½-ï¿½ï¿½ ï¿½ ï¿½ ï¿½ ï¿½ ï¿½-ï¿½-ï¿½ï¿½ ï¿½ï¿½ï¿½ï¿½ï¿½Oï¿½Oï¿½ ï¿½ ï¿½ï¿½ï¿½ï¿½ ï¿½-ï¿½-ï¿½ï¿½ ï¿½-ï¿½-ï¿½ï¿½ï¿½#ï¿½#ï¿½ ï¿½ï¿½%ï¿½%ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½'ï¿½'ï¿½ï¿½ï¿½!ï¿½!ï¿½ï¿½ ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½'ï¿½'ï¿½ï¿½ï¿½#ï¿½#ï¿½ ï¿½ï¿½%ï¿½%ï¿½ï¿½ï¿½>ï¿½>ï¿½ï¿½ï¿½>ï¿½>ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½Oï¿½Oï¿½ ï¿½Lï¿½":ï¿½0ï¿½3ï¿½ï¿½ï¿½ï¿½Cï¿½ï¿½ï¿½ï¿½ ï¿½ ï¿½3ï¿½ï¿½zï¿½ï¿½ ï¿½Gï¿½ï¿½ï¿½}ï¿½*ï¿½)ï¿½)ï¿½*ï¿½sï¿½ Hï¿½H,ï¿½+H,